DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 have been examined in this Final Rejection. Claims 1-20 are currently pending. Claims 10-20 are new.
Priority
Application 19/216,279 filed 05/22/2025 claims priority to provisional application 63/650,416 filed 05/22/2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a system for determining a probability of acceptance of a program.
For Claim 1:
[…] to perform operations comprising: retrieving curated program data corresponding to program acceptance criteria from a program database;
retrieving predefined data […] based on the program acceptance criteria, […];
responsive to the predefined data failing to satisfy all of the program acceptance criteria, triggering an […] query to retrieve secondary data from a domain-specific data source, of the plurality of decentralized domain-specific data sources, that is determined based on the program acceptance criteria; and
determining, […], a probability of program acceptance based on a comparison of the predefined data and the secondary data to the curated program acceptance criteria.
For Claim 3:
a processing system that implements a program probability of acceptance determination comprising:
[…] configured to: intake domain-specific data from a plurality of decentralized data sources […], wherein an impediment to access of one of the independent domain-specific layers does not impede access to another of the independent domain-specific layers;
[…] configured to: curate program data based on program acceptance criteria;
store the curated program data and corresponding curated program acceptance criteria at a program database;
a predefined data layer configured to localize a subset of high-frequency program criteria data for retrieval more proximal than the data mesh;
[…] configured to: retrieve predefined data […] based on the curated program acceptance criteria;
responsive to the predefined data failing to satisfy all of the curated program acceptance criteria, retrieve secondary data from one of the independent domain-specific layers of the data mesh that is determined based on the curated program acceptance criteria and determine, […], a probability of program acceptance based on a comparison of the predefined data and the secondary data to the curated program acceptance criteria.
For Claim 10:
retrieving, […], curated program data corresponding to program acceptance criteria from a program database;
retrieving, […], predefined data […] based on the program acceptance criteria, […];
responsive to the predefined data failing to satisfy all of the program acceptance criteria, triggering, […], an […] query to retrieve secondary data from one of the plurality of independent domain-specific layers that is determined based on the program acceptance criteria; and
determining, […], a probability of program acceptance based on a comparison of the predefined data and the secondary data to the curated program acceptance criteria.
The above limitations are reciting a process where a person (a patient seeking acceptance for treatment or aid with medical bills) applies for financial aid from a charitable organization and determining the probability of receiving the funding based on comparing the requirements to meet the guidelines with the persons information. This is a fundamental economic practice. As was stated in the specification in paragraph 002-003:
Often health insurance fails to cover the full payment of care for patient treatments, drugs, and other medical expenses. The patient is required to pay the remaining fees owed, which can be referred to as the out-of-pocket portion of the medical fee. Furthermore, the payments are often not required until after a treatment of series of treatments or medical services are performed. This can lead to the patient owing very large out-of-pocket sums. Fortunately, there are programs available to cover out of pocket costs for various patient populations (e.g., income-based threshold payments). Regardless of the existence of such philanthropic programs, patients often have difficulty identifying the appropriate programs that can provide financial aid and are therefore unable to access such sources of funding for medical expenses.
There is no dispute that obtaining information to determine if financial assistance for medical expenses is something that is a human activity and a financial practice. This defines a certain method of organizing human activities type of abstract idea. Accordingly, claims 1, 3, and 10 recite an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. Claims 1, 3, and 10 recites the additional elements of one or more processors (Claim 1, Claim 10), one or more storage devices (Claim 1), automated query (Claim 1, Claim 10), a decentralized data mesh layer (Claim 1, Claim 3, Claim 10), a predictive analysis model (Claim 1, Claim 3, and Claim 10), a data mesh engine (Claim 3), a program curation engine (Claim 3), a program determination engine (Claim 3), that implements the identified abstract idea. These additional elements are recited at a high-level of generality (i.e., one or more generic computers performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claims 1, 3, and 10 are directed to an abstract idea. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of one or more processors (Claim 1, Claim 10), one or more storage devices (Claim 1), automated query (Claim 1, Claim 10), a decentralized data mesh layer (Claim 1, Claim 3, Claim 10), a predictive analysis model (Claim 1, Claim 3, and Claim 10), a data mesh engine (Claim 3), a program curation engine (Claim 3), a program determination engine (Claim 3), to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such claims 1, 3, and 10 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 4-9, and 11-20 are similarly rejected because they either further define/narrow the abstract idea of independent claims 1, 3, and 10 as discussed above and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 4, merely describe(s) retrieving data based on failure of a match. Claim(s) 5, merely describe(s) determining if the probability of acceptance is greater than a threshold and provisioning a subset of data to a high confidence database based on the probability of the acceptance that is greater than the threshold probability of program acceptance. Claim(s) 6, merely describe(s) determining if the probability is lower than a threshold of acceptance, and provisioning a subset of the data to a lower probability database based on the probability of the program acceptance that is lower than the target threshold probability of program acceptance. Claim(s) 7, merely describe(s) generating a score that represents the probability of program acceptance as compared to a program and ranking a set of programs including the program based on the score. Claim(s) 8, merely describe(s) identifying relations between the score of a program and program characteristic and generating a prioritization hierarchy of program tasks for completion based on the identified relationships between the score of a program and program task requirements. Claim(s) 9, merely describe(s) the program value propositions being one or more of a value of a program award or an application time period representing a start date of a program application until a closing data of the program application. Claim(s) 13 merely describe(s) generating a forecast score that represents the probability of program acceptance and ranking a set of programs based on the forecast score. Claim(s) 14 merely describe(s) determining whether the probability of program acceptance is above or below a target threshold confidence interval, and provisioning a subset of at least one of the curated program data, the predefined data, or the secondary data to a high confidence insight module based on the probability of program acceptance being above the target threshold confidence interval, or to a low confidence insight module based on the probability of program acceptance being below the target threshold confidence interval. Claim(s) 15 merely describe(s) generating a plurality of tasks, and assigning a rank to each task of the plurality of tasks based on a plurality of weighted parameters comprising two or more of an approval rate, an award rate, a days-retroactive value, a program type, a financial class, and a task type. Claim(s) 16 merely describe(s) assigning the rank comprises applying a default weight to a parameter of the plurality of weighted parameters that has a missing value. Claim(s) 17 merely describe(s) wherein the days- retroactive value is computed as a system date less a date of service. Claim(s) 18 merely describe(s) ordering a task queue in descending order of the rank assigned to each task of the plurality of tasks, and surfacing the plurality of tasks at a user portal. Claim(s) 19 merely describe(s) responsive to identifying a plurality of matches for a patient, determining, a best match of the plurality of matches having a highest rank value, and recommending the best match. Claim(s) 20 merely describe(s) applying one or more customer exclusion to a program determined to have a likelihood of relevancy to a patient.
Dependent Claim(s) 2, 4 and 9, 11, 12, 19, and 20 recite limitations that further define the abstract idea noted in independent claims 1, 3, and 10. In addition, it recites the additional elements of a machine learning model, querying module, score relationship model, random forest algorithm, a matching module, and an exclusion module. The machine learning model, querying module, score relationship model, random forest algorithm, a matching module, and an exclusion module are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Dependent Claim(s) 4-8 includes the additional elements of a program determination engine, data mesh, and predictive analysis model. The program determination engine, data mesh, and predictive analysis model are analyzed in the same manner as the program determination engine, data mesh, and predictive analysis model in the independent claim and does not provide a practical application or significantly more for the same reasons above. Therefore claims 2, 4-9, and 11-20 are considered patent ineligible for the reasons given above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 4 recites the one of the independent domain-specific layers from which the secondary data, the one of the independent domain-specific layers from which the secondary data is retrieved and an order of query requests issued to the data mesh. It is not clear what the secondary data is providing in the first limitation. It is unsure if the claim recites two determinations of the domain specific layers, or if the same limitation is repeated twice, therefore making this claim indefinite.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 10, 11, 12, 13, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (US 20240144119 A1), Ritter (US20070156977A1) and in further view of Roman (US8868651B2).
Regarding Claim 1,
Zhao discloses, A system comprising: one or more processors; and one or more storage devices comprising processor executable instructions that, responsive to execution by the one or more processors, cause the system to perform operations comprising: (Zhao Par. 0009, 0091).
retrieving curated program data corresponding to program acceptance criteria from a program database; "For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc…. In some embodiments, a model is used to predict, extract, or infer job parameters from job requisitions. For example, a model may employ natural language processing to predict, extract, or infer job parameters from job requisitions" (Zhao Par. 0029). "The job requisition database 210 may be organized or batch job requisitions into one or more training data sets for the machine learning models of the job requisition-based machine learning model 130. The set of job requisitions may include information about each of the job requisitions, including pub2now time (e.g., a current age of the job requisition or a pub2now or age of the job requisition at the time an application was submitted), a number of open positions, a number of applications submitted, other job specific features (e.g., employer, location, etc.), other parameters, etc. The set of job requisitions may include, for example, information acquired from the job requisition by natural language processing" (Zhao Par. 0072). " The communication subsystem 1012 may retrieve one or more datasets from the requisition database 1040 or the candidate database 1050 for use in training or performing inference via the ML subsystem 1014 (e.g., using one or more machine-learning models as previously described). The requisition database 1040 and the candidate database 1050 may be a unified database or comprised of multiple data storage units" (Zhao Par. 0131).
determining, using a predictive analysis model, a probability of program acceptance based on a comparison of the predefined data and the secondary data to the curated program acceptance criteria. "In some embodiments, a model is used to predict a likelihood (e.g., a probability) of a specific job offer (e.g., corresponding to a specific job requisition) being extended to a candidate. For example, a model may be used to predict the probability of a specific job offer being extended to any member of a candidate pool. As another example, a model may be used to predict a likelihood of a specific job offer being extended to a specific candidate. In some embodiments, a model is used to predict a likelihood of a job offer being extended based on job requisition properties. For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc. In some embodiments, a model is used to predict a likelihood of a job offer being extended based on candidate properties. For example, a model may be used to predict a likelihood of a job offer being extended to a given candidate based on candidate properties including job parameters such as the job requisition age at the time of the candidate's application, the number of applications previous submitted to the job requisition at the time of the candidate's application, the candidate's experience level, the candidate's previous job offers, the candidate's previous rate of job offers, the candidate's location, the candidate's specialty experience, etc. In some embodiments, a model is used to predict, extract, or infer job parameters from job requisitions. For example, a model may employ natural language processing to predict, extract, or infer job parameters from job requisitions. In some embodiments, a model is used to predict, extract, or infer candidate parameters from candidate supplied information. For example, a model may be used to predict, extract, or infer candidate parameters from a candidate application currently submitted (or previously submitted) by a candidate" (Zhao Par. 0029).
Zhao discloses retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison. Zhao fails to disclose retrieving data from a decentralized data mesh layer based on criteria. Ritter discloses automated data field determination and insertion. Ritter further discloses,
retrieving predefined data from a predefined data layer based on the program acceptance criteria, wherein the predefined data layer localizes a subset of high-frequency program criteria data for retrieval more proximal than a decentralized data mesh layer comprising a plurality of decentralized domain-specific data sources; "Another common technique is an approach commonly known as auto-fill. This feature allows for the automatic filling of multiple data fields in a multi-field input form. For example, if the form includes address information, the insertion of a person's name in the first data field may trigger the auto-filling of the data into the rest of the form. This technique can be commonly found in internet web sites where a user fills out an electronic form, such as an order form. One technique may include locally saved software code, e.g. cookies, that enable the auto-fill of the requisite information… These auto-fill features can provide a valuable time saving resource, inserting commonly used customer information or other information into electronic forms, thereby improving system efficiency" (Ritter Par. 0004-0005).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao with retrieving predefined data from a predefined data layer based on the program acceptance criteria, wherein the predefined data layer localizes a subset of high-frequency program criteria data for retrieval more proximal than a decentralized data mesh layer comprising a plurality of decentralized domain-specific data sources of Ritter for valuable time saving improving system efficiency (Ritter Par. 0005).
The combination of Zhao and Ritter disclose retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison, and retrieving predefined data. The combination of Zhao and Ritter fail to disclose the predefined data failing to satisfy all of the program acceptance criteria. Roman discloses automatically populating users into groups of a web community. Roman further discloses,
responsive to the predefined data failing to satisfy all of the program acceptance criteria, triggering an automated query to retrieve secondary data from a domain-specific data source, of the plurality of decentralized domain-specific data sources, that is determined based on the program acceptance criteria; and "The profile module 141 may also interact with another database (not shown), such as a member, organization, or employee database, to obtain user profile information. The profile module 141 may create (e.g., automatically) user profiles using the information from this database. In addition or in the alternative, the profile module 141 may use information obtained from the user 101 during the registration process and information obtained from another database to create a user profile. For example, the profile module 141 may use registration information (e.g., name, e-mail address, or employee identifier) to find information regarding an employee in an employee database and populate at least a portion of the employee's user profile with information from the employee database. The profile module 141 may create or modify a user profile automatically in response to a new user 101 being added to the database or a change in information associated with a user 101 that is stored in the database" (Roman Col. 5 Lines 68-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao and Ritter with the predefined data failing to satisfy all of the program acceptance criteria, triggering an automated query to retrieve secondary data from a domain-specific data source, of the plurality of decentralized domain-specific data sources, that is determined based on the program acceptance criteria of Roman to automatically create profiled and populate information (Roman Col. 5 Lines 40-58).
Regarding Claim 2,
The combination of Zhao, Ritter, and Roman disclose the system of claim 1, as shown above. Zhao further discloses, training a machine learning model on high probability program acceptance data; "In some embodiments, a model provides one or more matched job requisitions to a candidate, in ranked or unranked order. In some embodiments, a model provides matched job requisitions for which a likelihood of an offer being extended is above a threshold, provides all job requisitions, provides a subset of job requisitions, provides additional job requisitions based on input from the candidate, etc" (Zhao Par. 0039). "In some embodiments, the job requisition-based machine learning model 130 may weight other example job information… An offer rate for a job requisition may be correlated with a historical offer rate for the facility of the job requisition. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively high for a candidate currently on assignment at the facility of the job requisition, with respect to job requisitions at other facilities. An offer rate for a job requisition may be relatively high for a candidate that the facility has previously hired, is currently on assignment with, etc. This may be because the facility is considered familiar with the candidate, that it is considered easier administratively to hire a returning candidate, etc. In yet another example, the job requisition-based machine learning model 130 may rank a job requisition (or candidate) relatively high for a candidate with relatively high historical offer rate (e.g., a sub2offer rate) with respect to other candidates. A candidate with a relatively high offer rate may, for example, be expected to maintain a high offer rate. Moreover, this may indicate that the candidate is well qualified. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively low for a job requisition with a longer pub2now time. This may be correlated to an increase in number of applications, and thus a decrease in job offer rate for any respective application, over time" (Zhao Par. 0053).
extracting insights from the machine learning model; " For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc." (Zhao Par. 0029). "For example, the candidate-based machine learning model 140 may provide after-submission information about a candidate application, such as likelihood of an offer being extended, etc. based on the application which was submitted" (Zhao Par. 0068).
adjusting one or more parameter of the predictive analysis model based on the extracted insights. "In some embodiments, a model is iteratively updated based on changes over time, such as changes in the set of job requisitions, new/updated application submissions, etc. In some embodiments, a model is used to update rankings of a set of job requisitions as new candidate applications are submitted for the set of job requisitions. For example, if one or more applications have been submitted or additional job requisitions supplied between a time A and a time B, a ranking of the set of job requisitions at a time A may be different than a ranking of the set of job requisitions at a time B. (Zhao Par. 0032). "For example, the candidate-based machine learning model 140 may provide after-submission information about a candidate application, such as likelihood of an offer being extended, etc. based on the application which was submitted. In some embodiments, a candidate-based machine learning model determines time-variable outputs. For example, the candidate-based machine learning model 140 may determine and update the likelihood of an offer being extended as a function of time (e.g., as the time from the application submission event increases). The likelihood of an offer being extended may decrease with time, such as more applications are submitted in response to the job requisition. However, unexpected results may occur. For example, the likelihood of an offer being extended may increase with time, as the job requisition is closed or a decision is made, and then decrease with time and an offer is extended to another candidate or the position is filled or cancelled" (Zhao Par. 0068).
Regarding Claim 10,
Zhao discloses, (New) A computer-implemented method comprising: retrieving, by a system operatively coupled to one or more processors, curated program data corresponding to program acceptance criteria from a program database; "For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc…. In some embodiments, a model is used to predict, extract, or infer job parameters from job requisitions. For example, a model may employ natural language processing to predict, extract, or infer job parameters from job requisitions" (Zhao Par. 0029). "The job requisition database 210 may be organized or batch job requisitions into one or more training data sets for the machine learning models of the job requisition-based machine learning model 130. The set of job requisitions may include information about each of the job requisitions, including pub2now time (e.g., a current age of the job requisition or a pub2now or age of the job requisition at the time an application was submitted), a number of open positions, a number of applications submitted, other job specific features (e.g., employer, location, etc.), other parameters, etc. The set of job requisitions may include, for example, information acquired from the job requisition by natural language processing" (Zhao Par. 0072). " The communication subsystem 1012 may retrieve one or more datasets from the requisition database 1040 or the candidate database 1050 for use in training or performing inference via the ML subsystem 1014 (e.g., using one or more machine-learning models as previously described). The requisition database 1040 and the candidate database 1050 may be a unified database or comprised of multiple data storage units" (Zhao Par. 0131). See also Zhao Par. 0009, 0091.
determining, by the system using a predictive analysis model, a probability of program acceptance based on a comparison of the predefined data and the secondary data to the curated program acceptance criteria. "In some embodiments, a model is used to predict a likelihood (e.g., a probability) of a specific job offer (e.g., corresponding to a specific job requisition) being extended to a candidate. For example, a model may be used to predict the probability of a specific job offer being extended to any member of a candidate pool. As another example, a model may be used to predict a likelihood of a specific job offer being extended to a specific candidate. In some embodiments, a model is used to predict a likelihood of a job offer being extended based on job requisition properties. For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc. In some embodiments, a model is used to predict a likelihood of a job offer being extended based on candidate properties. For example, a model may be used to predict a likelihood of a job offer being extended to a given candidate based on candidate properties including job parameters such as the job requisition age at the time of the candidate's application, the number of applications previous submitted to the job requisition at the time of the candidate's application, the candidate's experience level, the candidate's previous job offers, the candidate's previous rate of job offers, the candidate's location, the candidate's specialty experience, etc. In some embodiments, a model is used to predict, extract, or infer job parameters from job requisitions. For example, a model may employ natural language processing to predict, extract, or infer job parameters from job requisitions. In some embodiments, a model is used to predict, extract, or infer candidate parameters from candidate supplied information. For example, a model may be used to predict, extract, or infer candidate parameters from a candidate application currently submitted (or previously submitted) by a candidate" (Zhao Par. 0029).
Zhao discloses retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison. Zhao fails to disclose retrieving data from a decentralized data mesh layer based on criteria. Ritter discloses automated data field determination and insertion. Ritter further discloses,
retrieving, by the system, predefined data from a predefined data layer based on the program acceptance criteria, wherein the predefined data layer localizes a subset of high-frequency program criteria data for retrieval more proximal than a decentralized data mesh comprising a plurality of independent domain-specific layers; "Another common technique is an approach commonly known as auto-fill. This feature allows for the automatic filling of multiple data fields in a multi-field input form. For example, if the form includes address information, the insertion of a person's name in the first data field may trigger the auto-filling of the data into the rest of the form. This technique can be commonly found in internet web sites where a user fills out an electronic form, such as an order form. One technique may include locally saved software code, e.g. cookies, that enable the auto-fill of the requisite information… These auto-fill features can provide a valuable time saving resource, inserting commonly used customer information or other information into electronic forms, thereby improving system efficiency" (Ritter Par. 0004-0005).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao with retrieving predefined data from a predefined data layer based on the program acceptance criteria, wherein the predefined data layer localizes a subset of high-frequency program criteria data for retrieval more proximal than a decentralized data mesh layer comprising a plurality of decentralized domain-specific data sources of Ritter for valuable time saving improving system efficiency (Ritter Par. 0005).
The combination of Zhao and Ritter disclose retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison, and retrieving predefined data. The combination of Zhao and Ritter fail to disclose the predefined data failing to satisfy all of the program acceptance criteria. Roman discloses automatically populating users into groups of a web community. Roman further discloses,
responsive to the predefined data failing to satisfy all of the program acceptance criteria, triggering, by the system, an automated query to retrieve secondary data from one of the plurality of independent domain-specific layers that is determined based on the program acceptance criteria; and "The profile module 141 may also interact with another database (not shown), such as a member, organization, or employee database, to obtain user profile information. The profile module 141 may create (e.g., automatically) user profiles using the information from this database. In addition or in the alternative, the profile module 141 may use information obtained from the user 101 during the registration process and information obtained from another database to create a user profile. For example, the profile module 141 may use registration information (e.g., name, e-mail address, or employee identifier) to find information regarding an employee in an employee database and populate at least a portion of the employee's user profile with information from the employee database. The profile module 141 may create or modify a user profile automatically in response to a new user 101 being added to the database or a change in information associated with a user 101 that is stored in the database" (Roman Col. 5 Lines 68-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao and Ritter with the predefined data failing to satisfy all of the program acceptance criteria, triggering an automated query to retrieve secondary data from a domain-specific data source, of the plurality of decentralized domain-specific data sources, that is determined based on the program acceptance criteria of Roman to automatically create profiled and populate information (Roman Col. 5 Lines 40-58).
Regarding Claim 11,
The combination of Zhao, Ritter, and Roman disclose the system of claim 10, as shown above. Zhao further discloses, (New) The computer-implemented method of claim 10, further comprising: training a machine learning model on high probability program acceptance data; "In some embodiments, a model provides one or more matched job requisitions to a candidate, in ranked or unranked order. In some embodiments, a model provides matched job requisitions for which a likelihood of an offer being extended is above a threshold, provides all job requisitions, provides a subset of job requisitions, provides additional job requisitions based on input from the candidate, etc" (Zhao Par. 0039). "In some embodiments, the job requisition-based machine learning model 130 may weight other example job information… An offer rate for a job requisition may be correlated with a historical offer rate for the facility of the job requisition. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively high for a candidate currently on assignment at the facility of the job requisition, with respect to job requisitions at other facilities. An offer rate for a job requisition may be relatively high for a candidate that the facility has previously hired, is currently on assignment with, etc. This may be because the facility is considered familiar with the candidate, that it is considered easier administratively to hire a returning candidate, etc. In yet another example, the job requisition-based machine learning model 130 may rank a job requisition (or candidate) relatively high for a candidate with relatively high historical offer rate (e.g., a sub2offer rate) with respect to other candidates. A candidate with a relatively high offer rate may, for example, be expected to maintain a high offer rate. Moreover, this may indicate that the candidate is well qualified. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively low for a job requisition with a longer pub2now time. This may be correlated to an increase in number of applications, and thus a decrease in job offer rate for any respective application, over time" (Zhao Par. 0053).
extracting insights from the machine learning model; and "For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc." (Zhao Par. 0029). "For example, the candidate-based machine learning model 140 may provide after-submission information about a candidate application, such as likelihood of an offer being extended, etc. based on the application which was submitted" (Zhao Par. 0068).
iteratively adjusting one or more parameter or hyper-parameter of the predictive analysis model based on the extracted insights. "In some embodiments, a model is iteratively updated based on changes over time, such as changes in the set of job requisitions, new/updated application submissions, etc. In some embodiments, a model is used to update rankings of a set of job requisitions as new candidate applications are submitted for the set of job requisitions. For example, if one or more applications have been submitted or additional job requisitions supplied between a time A and a time B, a ranking of the set of job requisitions at a time A may be different than a ranking of the set of job requisitions at a time B. (Zhao Par. 0032). "For example, the candidate-based machine learning model 140 may provide after-submission information about a candidate application, such as likelihood of an offer being extended, etc. based on the application which was submitted. In some embodiments, a candidate-based machine learning model determines time-variable outputs. For example, the candidate-based machine learning model 140 may determine and update the likelihood of an offer being extended as a function of time (e.g., as the time from the application submission event increases). The likelihood of an offer being extended may decrease with time, such as more applications are submitted in response to the job requisition. However, unexpected results may occur. For example, the likelihood of an offer being extended may increase with time, as the job requisition is closed or a decision is made, and then decrease with time and an offer is extended to another candidate or the position is filled or cancelled" (Zhao Par. 0068).
Regarding Claim 12,
The combination of Zhao, Ritter, and Roman disclose the system of claim 11, as shown above. Zhao further discloses, (New) The computer-implemented method of claim 11, wherein the machine learning model comprises a random forest algorithm. "The machine learning model may be an ensemble of machine learning models. The machine learning model may be trained or at least partially untrained. The machine learning model may be any appropriate type of machine learning model, including a decision tree model, a random forest model, a gradient boosted decision tree, a neural network, a classification model, a regression model, a supervised learning model, etc. The machine learning model may contain any appropriate arrangement of layers, inputs, outputs, weightings, etc" (Zhao Par. 0107).
Regarding Claim 13,
The combination of Zhao, Ritter, and Roman disclose the system of claim 10, as shown above. Zhao further discloses, (New) The computer-implemented method of claim 10, further comprising: generating a forecast score that represents the probability of program acceptance; and "In some embodiments, a measure of outcome success for the set of requisitions is determined based on the machine learning model (block 506). The measure of outcome success may be the likelihood of a job offer being extended, an acceptance of a proposal, etc. The measure of outcome success may be expressed as a score (for example, a numerical value between 0 and 1 or between 0 and 100), a value level (for example, good, medium, bad), a probability, etc. The measure of outcome success may include a confidence interval or other measure of the quality of the determination. The measure of outcome success may be a range. The measure of outcome success may include multiple measures of outcome success, such as from different machine learning models of an ensemble of machine learning models, or may average (including using a weighted average) multiple measures of outcome success to generate a total measure of outcome success. The measure of outcome success may be different for each of the set of requisitions or may be determined for multiple requisitions together" (Zhao Par. 0094).
ranking a set of programs based on the forecast score. "In some embodiments, the set of requisitions is ranked based on the measure of outcome success (block 508). The set of requisitions may be ordinally ranked from most likelihood of success to least. The set of requisitions may also be ranked for all requisitions with a likelihood of success greater than a minimum threshold. The set of requisitions may be batched into requisitions with high likelihood of success, medium likelihood of success, etc." (Zhao Par. 0095).
Regarding Claim 14,
The combination of Zhao, Ritter, and Roman disclose the system of claim 10, as shown above. Zhao further discloses, (New) The computer-implemented method of claim 10, further comprising: determining whether the probability of program acceptance is above or below a target threshold confidence interval; and "For example, a model may optimize candidate and job requisition matching based on full employment goals (e.g., a job requisitions for substantially all candidates wherein the likelihood of an offering being extended is maximized over the candidate), based on total compensation goals (e.g., matching a candidate with a job offer for which the compensation is the highest for which the probability of a job offer being extended at least exceeds a threshold), etc." (Zhao Par. 0035).
provisioning a subset of at least one of the curated program data, the predefined data, or the secondary data to a high confidence insight module based on the probability of program acceptance being above the target threshold confidence interval, or to a low confidence insight module based on the probability of program acceptance being below the target threshold confidence interval. "In some embodiments, a model provides one or more matched job requisitions to a candidate, in ranked or unranked order. In some embodiments, a model provides matched job requisitions for which a likelihood of an offer being extended is above a threshold, provides all job requisitions, provides a subset of job requisitions, provides additional job requisitions based on input from the candidate, etc" (Zhao Par. 0039). "In some embodiments, the job requisition-based machine learning model 130 may weight other example job information… An offer rate for a job requisition may be correlated with a historical offer rate for the facility of the job requisition. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively high for a candidate currently on assignment at the facility of the job requisition, with respect to job requisitions at other facilities. An offer rate for a job requisition may be relatively high for a candidate that the facility has previously hired, is currently on assignment with, etc. This may be because the facility is considered familiar with the candidate, that it is considered easier administratively to hire a returning candidate, etc. In yet another example, the job requisition-based machine learning model 130 may rank a job requisition (or candidate) relatively high for a candidate with relatively high historical offer rate (e.g., a sub2offer rate) with respect to other candidates. A candidate with a relatively high offer rate may, for example, be expected to maintain a high offer rate. Moreover, this may indicate that the candidate is well qualified. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively low for a job requisition with a longer pub2now time. This may be correlated to an increase in number of applications, and thus a decrease in job offer rate for any respective application, over time" (Zhao Par. 0053).
Regarding Claim 20,
The combination of Zhao, Ritter, and Roman disclose the system of claim 10, as shown above. Zhao further discloses, (New) The computer-implemented method of claim 10, further comprising applying, via an exclusion module, one or more customer exclusion to a program determined to have a likelihood of relevancy to a patient. "In some embodiments, such prioritization may be enabled by default, such as regardless of any other filters applied to job requisitions. For example, such prioritization may be used to rank jobs even if other search criteria are used—such as location, specialization, etc., where jobs with a relatively high likelihood of a job offer being extended are displayed before jobs with a relatively better match to search criteria. In an illustrative example, a location search criterion may be used in combination with such prioritization and job requisitions may be ranked in the following order: (1) job in desired location with high likelihood of job offer being extended; (2) job close to desired location with high likelihood of job offer being extended; (3) job in desired location with low likelihood of job offer being extended, etc., where relative weight between search criterion and likelihood of a job offer being extended may vary between search criteria" (Zhao Par. 0059).
Claim(s) 3, 5, 6, 7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao (US 20240144119 A1), Ritter (US20070156977A1), in view of Roman (US8868651B2), and in further view of Wider, Arif (Decentralized Data Governance as Part of a Data Mesh Platform: Concepts and Approaches).
Regarding Claim 3,
Zhao discloses, (Currently Amended) A system comprising: a processing system that implements a program probability of acceptance determination comprising: "In some embodiments, a job requisition model (or “model”) is used to predict a likelihood (e.g., a probability) of a job offering being extended" (Zhao Par. 0028).
a program curation engine configured to: curate program data based on program acceptance criteria; "For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc…. In some embodiments, a model is used to predict, extract, or infer job parameters from job requisitions. For example, a model may employ natural language processing to predict, extract, or infer job parameters from job requisitions" (Zhao Par. 0029).
store the curated program data and corresponding curated program acceptance criteria at a program database; "The job requisition database 210 may be organized or batch job requisitions into one or more training data sets for the machine learning models of the job requisition-based machine learning model 130. The set of job requisitions may include information about each of the job requisitions, including pub2now time (e.g., a current age of the job requisition or a pub2now or age of the job requisition at the time an application was submitted), a number of open positions, a number of applications submitted, other job specific features (e.g., employer, location, etc.), other parameters, etc. The set of job requisitions may include, for example, information acquired from the job requisition by natural language processing" (Zhao Par. 0072).
a program determination engine configured to: "In some embodiments, a job requisition model (or “model”) is used to predict a likelihood (e.g., a probability) of a job offering being extended" (Zhao Par. 0028).
retrieve predefined data from the predefined data layer based on the curated program acceptance criteria; and " For example, a model may be used to predict a likelihood of a job offer being extended to a given candidate based on candidate properties including job parameters such as the job requisition age at the time of the candidate's application, the number of applications previous submitted to the job requisition at the time of the candidate's application, the candidate's experience level, the candidate's previous job offers, the candidate's previous rate of job offers, the candidate's location, the candidate's specialty experience, etc… In some embodiments, a model is used to predict, extract, or infer candidate parameters from candidate supplied information. For example, a model may be used to predict, extract, or infer candidate parameters from a candidate application currently submitted (or previously submitted) by a candidate. In some embodiments, a model is used to predict, extract, or infer candidate parameters from candidate interactions with job requisitions." (Zhao Par. 0029).
determine, using a predictive analysis model, a probability of program acceptance based on a comparison of the predefined data and the secondary data to the curated program acceptance criteria. "In some embodiments, a model is used to predict a likelihood (e.g., a probability) of a specific job offer (e.g., corresponding to a specific job requisition) being extended to a candidate. For example, a model may be used to predict the probability of a specific job offer being extended to any member of a candidate pool. As another example, a model may be used to predict a likelihood of a specific job offer being extended to a specific candidate. In some embodiments, a model is used to predict a likelihood of a job offer being extended based on job requisition properties. For example, a model may be used to predict a likelihood of a job offer being extended for a job based on job requisition properties including job parameters such as job requisition age, the employer offering the job and the employer's propensity for extending offers in the past, the status of the employer as a client of the candidate pool, the job location, the job specialty, the job rate, the total number of applications previously submitted to the job requisition, etc. In some embodiments, a model is used to predict a likelihood of a job offer being extended based on candidate properties. For example, a model may be used to predict a likelihood of a job offer being extended to a given candidate based on candidate properties including job parameters such as the job requisition age at the time of the candidate's application, the number of applications previous submitted to the job requisition at the time of the candidate's application, the candidate's experience level, the candidate's previous job offers, the candidate's previous rate of job offers, the candidate's location, the candidate's specialty experience, etc. In some embodiments, a model is used to predict, extract, or infer job parameters from job requisitions. For example, a model may employ natural language processing to predict, extract, or infer job parameters from job requisitions. In some embodiments, a model is used to predict, extract, or infer candidate parameters from candidate supplied information. For example, a model may be used to predict, extract, or infer candidate parameters from a candidate application currently submitted (or previously submitted) by a candidate" (Zhao Par. 0029).
Zhao discloses retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison. Zhao fails to disclose retrieving data from a decentralized data mesh layer based on criteria, a data mesh engine configured to: intake domain-specific data from a plurality of decentralized data sources organized as independent domain-specific layers of a data mesh, wherein an impediment to access of one of the independent domain-specific layers does not impede access to another of the independent domain-specific layers, and a predefined data layer configured to localize a subset of high-frequency program criteria data for retrieval more proximal than the data mesh. Ritter discloses automated data field determination and insertion. Ritter further discloses,
a predefined data layer configured to localize a subset of high-frequency program criteria data for retrieval more proximal than the data mesh; "Another common technique is an approach commonly known as auto-fill. This feature allows for the automatic filling of multiple data fields in a multi-field input form. For example, if the form includes address information, the insertion of a person's name in the first data field may trigger the auto-filling of the data into the rest of the form. This technique can be commonly found in internet web sites where a user fills out an electronic form, such as an order form. One technique may include locally saved software code, e.g. cookies, that enable the auto-fill of the requisite information… These auto-fill features can provide a valuable time saving resource, inserting commonly used customer information or other information into electronic forms, thereby improving system efficiency" (Ritter Par. 0004-0005).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao with a predefined data layer configured to localize a subset of high-frequency program criteria data for retrieval more proximal than the data mesh of Ritter for valuable time saving improving system efficiency (Ritter Par. 0005).
The combination of Zhao and Ritter disclose retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison, and retrieving predefined data. The combination of Zhao and Ritter fail to disclose the responsive to the predefined data failing to satisfy all of the curated program acceptance criteria, retrieve secondary data from one of the independent domain-specific layers of the data mesh that is determined based on the curated program acceptance criteria, and a data mesh engine configured to: intake domain-specific data from a plurality of decentralized data sources organized as independent domain-specific layers of a data mesh, wherein an impediment to access of one of the independent domain-specific layers does not impede access to another of the independent domain-specific layers. Roman discloses automatically populating users into groups of a web community. Roman further discloses,
responsive to the predefined data failing to satisfy all of the program acceptance criteria, triggering an automated query to retrieve secondary data from a domain-specific data source, of the plurality of decentralized domain-specific data sources, that is determined based on the program acceptance criteria; and "The profile module 141 may also interact with another database (not shown), such as a member, organization, or employee database, to obtain user profile information. The profile module 141 may create (e.g., automatically) user profiles using the information from this database. In addition or in the alternative, the profile module 141 may use information obtained from the user 101 during the registration process and information obtained from another database to create a user profile. For example, the profile module 141 may use registration information (e.g., name, e-mail address, or employee identifier) to find information regarding an employee in an employee database and populate at least a portion of the employee's user profile with information from the employee database. The profile module 141 may create or modify a user profile automatically in response to a new user 101 being added to the database or a change in information associated with a user 101 that is stored in the database" (Roman Col. 5 Lines 68-54).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao and Ritter with in response to the predefined data failing to satisfy all of the program acceptance criteria, triggering an automated query to retrieve secondary data from a domain-specific data source, of the plurality of decentralized domain-specific data sources, that is determined based on the program acceptance criteria of Roman to automatically create profiled and populate information (Roman Col. 5 Lines 40-58).
The combination of Zhao, Ritter, and Roman disclose a system of retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison, and retrieving predefined data. The combination of Zhao, Ritter, and Roman fail to disclose a data mesh engine configured to: intake domain-specific data from a plurality of decentralized data sources organized as independent domain-specific layers of a data mesh, wherein an impediment to access of one of the independent domain-specific layers does not impede access to another of the independent domain-specific layers. Wider, however, discloses a data mesh approach. Wider further discloses, a data mesh engine configured to: intake domain-specific data from a plurality of decentralized data sources organized as independent domain-specific layers of a data mesh, wherein an impediment to access of one of the independent domain-specific layers does not impede access to another of the independent domain-specific layers; Wider, Page 3, Figure. 1
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the matching system of Zhao, Ritter, and Roman with a data mesh engine to intake domain-specific data from a plurality of decentralized data sources organized as independent domain-specific layers of a data mesh of Wider to make building complex data solutions as easy as possible to reduce friction by automating processes to make it easier for teams to exchange data with each other (Wider Page 4).
Regarding Claim 5,
The combination of Zhao, Ritter, Roman and Wider disclose the system of claim 3, as shown above. Zhao further discloses, wherein the program determination engine is further configured to: determine whether the probability of program acceptance is greater than a target threshold probability of program acceptance; and "For example, a model may optimize candidate and job requisition matching based on full employment goals (e.g., a job requisitions for substantially all candidates wherein the likelihood of an offering being extended is maximized over the candidate), based on total compensation goals (e.g., matching a candidate with a job offer for which the compensation is the highest for which the probability of a job offer being extended at least exceeds a threshold), etc." (Zhao Par. 0035).
provision a subset of any one of the program data, the secondary data, or the predefined data to a high confidence prediction engine database based on the probability of the program acceptance that is greater than the target threshold probability of program acceptance. "In some embodiments, a model provides one or more matched job requisitions to a candidate, in ranked or unranked order. In some embodiments, a model provides matched job requisitions for which a likelihood of an offer being extended is above a threshold, provides all job requisitions, provides a subset of job requisitions, provides additional job requisitions based on input from the candidate, etc" (Zhao Par. 0039). "In some embodiments, the job requisition-based machine learning model 130 may weight other example job information… An offer rate for a job requisition may be correlated with a historical offer rate for the facility of the job requisition. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively high for a candidate currently on assignment at the facility of the job requisition, with respect to job requisitions at other facilities. An offer rate for a job requisition may be relatively high for a candidate that the facility has previously hired, is currently on assignment with, etc. This may be because the facility is considered familiar with the candidate, that it is considered easier administratively to hire a returning candidate, etc. In yet another example, the job requisition-based machine learning model 130 may rank a job requisition (or candidate) relatively high for a candidate with relatively high historical offer rate (e.g., a sub2offer rate) with respect to other candidates. A candidate with a relatively high offer rate may, for example, be expected to maintain a high offer rate. Moreover, this may indicate that the candidate is well qualified. In another example, the job requisition-based machine learning model 130 may rank a job requisition relatively low for a job requisition with a longer pub2now time. This may be correlated to an increase in number of applications, and thus a decrease in job offer rate for any respective application, over time" (Zhao Par. 0053).
Regarding Claim 6,
The combination of Zhao, Ritter, Roman and Wider disclose the system of claim 3, as shown above. Zhao further discloses, wherein the program determination engine is further configured to: determine whether the probability of program acceptance is lower than a target threshold probability of program acceptance; and " In some embodiments, the job requisition-based machine learning model 130 removes applications that correspond to candidates with a job rate (e.g., a job offer extension rate or job offer acceptance rate) below a threshold after at least a given number of associated job events. For example, applications from a candidate with a job offer extension rate of approximately zero (e.g., less than 1%) may be removed, where the candidate has submitted greater than a threshold number of applications (e.g., more than 80 applications)" (Zhao Par. 0054).
provision a subset of the program data to a lower probability prediction engine database based on the probability of the program acceptance that is lower than the target threshold probability of program acceptance. "In some embodiments, a model provides one or more matched job requisitions to a candidate, in ranked or unranked order. In some embodiments, a model provides matched job requisitions for which a likelihood of an offer being extended is above a threshold, provides all job requisitions, provides a subset of job requisitions, provides additional job requisitions based on input from the candidate, etc" (Zhao Par. 0039). "In some embodiments, the job requisition-based machine learning model 130 weights job information for use in generating output information, such as a ranking of a set of jobs or job requisitions. …..Such job information may indicate more applications may be submitted for such as job requisition and that receiving a job offer based on an application may be less likely. In some embodiments, job information is weighted such that a job is ranked lower or higher than similar jobs, where similar jobs may be jobs which have similar job information except for the specific job information described (e.g., which may share pieces of job information and vary substantially only in the identified piece of job information). For example, a job requisition having a relatively higher pay rate may be ranked lower than a job requisition having a relatively lower pay rate. This may be correlated to popularity, where, for example, a job requisition with a higher pay rate is expected to be more popular and competitive, lending itself to a lower likelihood of extending an offer to a given candidate" (Zhao Par. 0051, See also Par. 0052).
Regarding Claim 7,
The combination of Zhao, Ritter, Roman and Wider disclose the system of claim 3, as shown above. Zhao further discloses, wherein the program determination engine is further configured to: generate a forecast score that represents the probability of program acceptance as compared to a program; and "In some embodiments, a measure of outcome success for the set of requisitions is determined based on the machine learning model (block 506). The measure of outcome success may be the likelihood of a job offer being extended, an acceptance of a proposal, etc. The measure of outcome success may be expressed as a score (for example, a numerical value between 0 and 1 or between 0 and 100), a value level (for example, good, medium, bad), a probability, etc. The measure of outcome success may include a confidence interval or other measure of the quality of the determination. The measure of outcome success may be a range. The measure of outcome success may include multiple measures of outcome success, such as from different machine learning models of an ensemble of machine learning models, or may average (including using a weighted average) multiple measures of outcome success to generate a total measure of outcome success. The measure of outcome success may be different for each of the set of requisitions or may be determined for multiple requisitions together" (Zhao Par. 0094).
ranking a set of programs including the program based on the forecast score. "In some embodiments, the set of requisitions is ranked based on the measure of outcome success (block 508). The set of requisitions may be ordinally ranked from most likelihood of success to least. The set of requisitions may also be ranked for all requisitions with a likelihood of success greater than a minimum threshold. The set of requisitions may be batched into requisitions with high likelihood of success, medium likelihood of success, etc." (Zhao Par. 0095).
Regarding Claim 9,
The combination of Zhao, Ritter, Roman and Wider disclose the system of claim 7, as shown above. Zhao further discloses, wherein the program is associated with one or more program value propositions, the one or more program value propositions being any one or more of a value of a program award or an application time period representing a start date of a program application until a closing date of the program application. "The acquisition time stamp may, for example, correspond to the time that the published data corresponding to job 1 120a was scraped from the website, or the time that the published data corresponding to job 1 120a is input into the job requisition-based machine learning model 130. In such an embodiment, age may correspond to the amount of time between the given time of the publishing of job 1 120a on the employer website and the acquisition time stamp of the job 1 120a affixed by the job requisition-based machine learning model 130" (Zhao Par. 0044).
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao (US 20240144119 A1), Ritter (US20070156977A1), in view of Roman (US8868651B2), in view of Wider, Arif (Decentralized Data Governance as Part of a Data Mesh Platform: Concepts and Approaches), and in further view of Hanby (US 20230065120 A1).
Regarding Claim 8,
The combination of Zhao, Ritter, Roman and Wider disclose the system of claim 7, as shown above. Zhao further discloses, wherein the program determination engine is further configured to: identify relationships between the forecast score of a program and program characteristic based on a score relationship model; and "In some embodiments, a measure of outcome success for the set of requisitions is determined based on the machine learning model (block 506). The measure of outcome success may be the likelihood of a job offer being extended, an acceptance of a proposal, etc. The measure of outcome success may be expressed as a score (for example, a numerical value between 0 and 1 or between 0 and 100), a value level (for example, good, medium, bad), a probability, etc. The measure of outcome success may include a confidence interval or other measure of the quality of the determination. The measure of outcome success may be a range. The measure of outcome success may include multiple measures of outcome success, such as from different machine learning models of an ensemble of machine learning models, or may average (including using a weighted average) multiple measures of outcome success to generate a total measure of outcome success. The measure of outcome success may be different for each of the set of requisitions or may be determined for multiple requisitions together" (Zhao Par. 0094). "Wherein the machine learning model is trained to determine measures of outcome success based on the job requisition ages of the job requisitions, and wherein the set of requisitions are ranked based on job requisition ages" (Zhao Par. 0156). "In some embodiments, a “submission” age is determined, which corresponds to an age of the job requisition at the time of an application submission event. For example, the submission age of the requisition publishing event 104a at the application submission event 106a and 106b may correspond to an age of the job requisition at the time of the submission or “pub2sub” time 112a and 112b, respectively. The submission events 106a and 106b may each be associated with a respective submission time (e.g., application submission time stamp). The submission events 106a and 106b may also be associated with a respective publishing time (e.g., job publishing time stamps for publishing events 104a and 104b, respectively). For example, pub2sub 112a may correspond to the time difference between the requisition publishing event 104a of the job 1 120a and the application submission event 106a of application 1a 108a" (Zhao Par. 0047).
Zhao discloses identifying a relationship between a score of a program and characteristics based on a model. The combination of Zhao, Ritter, Roman and Wider fail to disclose generating a prioritization hierarchy of program tasks for completion based on the identified relationships. Hanby, however, discloses resource management and comparing tasks with existing tasks. Hanby, further discloses generate a prioritization hierarchy of program tasks for completion based on the identified relationships between the forecast score of a program and program task requirements "The system 100, in some embodiments, also comprises a prioritization system 110 that is adapted to execute the machine learning-based and artificial intelligence-based algorithms to prioritize and order task to generate a Sequence Schematic by the system 100. As such, the prioritization system 110 may implement supervised, semi-supervised, or unsupervised machine learning algorithms, and may be further trained by such that it can predict strategy, emotional drivers associated with one or more users, urgency factors, and other traits impact the prioritization of tasks. The result may be the creation of a more optimized ordering of tasks in the system. The prioritization system 110 may also be able to guide and facilitate the user's prioritization of new tasks as they are added to the system. The prioritization system 110 may output a contextualized Sequence Schematic which can be utilized by other parts of the system 100, and in some embodiments may be formatted as shown in FIG. 2" (Hanby Par. 0069).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the matching system of Zhao, Ritter, Roman and Wider with generating a prioritization hierarchy of tasks for completion based on the identified relationships between the score of a program and program task requirements of Hanby to automate processes for connecting active objectives in queue directly with a Workspace used for the collection, ideation, and collaboration, while simultaneously factoring relationship models between organizational strategic objectives and sub-objectives (Hanby Par. 0005).
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao (US 20240144119 A1), Ritter (US20070156977A1), in view of Roman (US8868651B2), and in further view of Salazar (US 20160371439 A1).
Regarding Claim 19,
The combination of Zhao, Ritter, and Roman disclose a system of retrieving curated program data corresponding to criteria from a database and determining a probability of acceptance based on a comparison, and retrieving predefined data. The combination of Zhao, Ritter, and Roman fail to disclose in response to identifying a plurality of matches for a patient, determining, via a matching module, a best match of the plurality of matches having a highest rank value; and recommending the best match. Salazar discloses a patient matching system. Salazar further discloses, (New) The computer-implemented method of claim 10, further comprising: responsive to identifying a plurality of matches for a patient, determining, via a matching module, a best match of the plurality of matches having a highest rank value; and recommending the best match. "In one embodiment, matching providers are ranked by the ranking module 350 based on a match strength of each matching provider. Match strength may be determined based on which provider information is compatible with patient information and a degree to which the information is compatible or incompatible. For example, the matching module 340 may return a list of providers that can meet the patient's medical needs, and the ranking module 350 may determine which of the providers best match the patient's preferences. The ranking module 350 may select and return the highest ranked matching provider, or may return a ranked list of all matching providers. In one embodiment, the device interface module 330 sends a confirmation request to a matching provider. A confirmation request may include patient information, and it may require that the provider confirm the match prior to notifying the requesting patient of the match. An example matching process is discussed in more detail with respect to FIG. 4 below" (Salazar Par. 0046).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the matching system of Zhao, Ritter, and Roman with determining a best match of the plurality of matches having a highest rank value and recommending the best match of Salazar since traveling to medical clinics can be costly, time-consuming, and inconvenient for patients, and a patient may be unable to effectively choose a doctor for the house call and schedule the doctor's visit (Salazar Par. 0003).
Subject Matter Distinguishable From Prior Art
After conducting different searches in PE2E Search, Similarity Search, Google, and IP.com, it appears that the cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features of querying retrieval of secondary data from the one of the independent layers from which the secondary data is retrieved and an order of query requests issued to the data mesh and assigning a rank to each task of the plurality of tasks based on a plurality of weighted parameters comprising two or more of an approval rate, award rate, a days-retroactive value, a program type, a financial class, and a task type, in combination with the other claimed limitations. Therefore Claim 4, and Claim 15 are allowable over prior art. Claims 16-18 are also allowable due to their dependencies on Claim 15.
Response to Arguments
Applicant's arguments filed 06/26/2026 with respect to the drawing objections, have been fully considered and are persuasive. The objections to the drawings have been withdrawn in light of the amendments.
Applicant's arguments filed 06/26/2026 with respect to 35 U.S.C. § 112, have been fully considered and are persuasive. The previous 35 U.S.C. § 112(b) rejection has been withdrawn in light of the amendments.
Applicant's arguments filed 06/26/2026 with respect to 35 U.S.C. § 101, have been fully considered but they are not persuasive. Applicant argues that the amended claims recite specific technical architecture and a specific data-retrieval operation which are not generic recitations or a mere instruction to “apply” an idea, rather they define a particular arrangement of components and a particular ordered data flow. The Examiner respectfully disagrees. The additional elements of one or more processors, one or more storage devices, a decentralized data mesh layer, a predictive analysis model, a data mesh engine, a program curation engine, a program determination engine, and data mesh are recited at a high level of generality and do not amount to a practical application that integrates the abstract idea into a specific technical improvement in computer functionality or another technology. The claimed features do not reflect an improvement to technology used to retrieve and process the data but rather use that technology as a tool to perform the abstract analysis. Therefore, the claims do not recite significantly more.
The Examiner acknowledges Applicant’s argument that the claim yields concrete technical benefits described in the specification such as “faster and consistent access of domain data”, “if a particular domain of decentralized data is impeded from access, other domains can still be accessible”, “single point failures risks” are reduced, and avoiding “over-provisioning data and under-utilizing resources” as reduced latency, fault tolerance, and resource optimization improvements, however, Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the generic computing components that results from the implementation of Applicant’s claim. There is no indication that the generic computing components are made to run faster, more efficiently, or utilize less power. In fact, the computer may be caused to operate slower and less efficiently through the implementation of Applicant’s claimed invention; we do not know and therefore it can be asserted. The claim recites the desired result without specifying a technical improvement in how the computer accomplishes the reduced latency, fault tolerance, and resource optimization.
The Applicant further argues for the same reasons, the specific, ordered combination recited, the proximal localization of high-frequency data in a predefined layer, a decentralized mesh of independent fault-isolated domain layers, and conditional criteria-driven retrieval from a particular domain, is a non-conventional and non-generic arrangement that supplies an inventive concept. The Examiner respectfully disagrees. Examiner notes that the claims do not recite an inventive concept sufficient to transform the judicial exception into patent eligible subject matter. Rather, the claimed processors, storage devices, decentralized data mesh layer, predictive analysis model, data mesh engine, program curation engine, program determination engine, and data mesh constitute generic computer functions used to implement the abstract idea and therefore do not amount to significantly more under Step 2B. Thus, the claims do not amount to a technological improvement and fail to provide an inventive concept.
The Applicant further argues that the cited art does not teach the amended independent claims. The rejection has been updated above in light of the amendments; therefore, this argument is moot. The 103 rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.M.K./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
